Abstract:
Bearings constitute a majority of the components found in rotating machines.
Though inexpensive, their failure can result in unnecessary downtime, losses in
production, and propagation of failure to other critical components leading to
expensive maintenance actions. Most of these rotating machinery are operated under
adverse and varying conditions which result in difficulty in defining health indices
from condition monitoring data. Predicting the failure of such machines is crucial
to determine when the maintenance is required thereby leading to a reduction in
maintenance costs and an improvement in the safety and reliability of the machines.
Therefore, techniques for condition monitoring of rotating machinery operated
under non-stationary conditions are necessary. This work employed a model-based
condition monitoring approach to predict the failure of rotating machinery under
non-stationary conditions. One of the advantages of model-based approach is the
ability to incorporate physical understanding of the system monitoring. Firstly,
the vibration model for rolling element bearing with fault was constructed in
MATLAB/Simulink environment. An automatic parameter identification based on
Particle Swarm Optimisation (PSO) algorithm was employed to identify the dynamic
parameters of a rolling element bearing due to its ease of implementation and
rapid convergence property. The optimized bearing parameters were then used in
diagnosing bearing faults. To evaluate the feasibility of this approach, two publicly
available data sets were employed. The results showed an improved average accuracy
of 99.67% and 99.2% for bearing faults of Case Western Reserve University and
University of Paderborn datasets, respectively. Additionally, the bearing model with
estimated parameters was used to generate degradation data by varying the fault
depth. Feature extraction was carried out where Root Mean Square (RMS) was
determined as the appropriate health indicator. Lastly, Paris degradation model was
employed to determine bearing damage and its evolution with time while factoring
in speed, applied load, and bearing geometry. The remaining useful life of the
bearing was found to be 1598 cycles. The prediction results and evaluation indexes
demonstrated the effectiveness and superiority of the proposed method.
